All previous contemplations were based on the idea that customers would be served in exactly the order in which they arrived. But is that an optimal procedure? The idea behind sequencing (which is also called triaging or prioritizing) is to determine, who should be served first, which is not always the customer who came in first (e.g. emergencies in a hospital). There are four basic models:

(1) First come-first-serve


This quite self-explanatory model (which is also known as FIFO = first in – first out) is easy to implement, seems fair and has the lowest variance of waiting time.

(2) Sequencing based on customer importance


Examples for this model are the prioritized treatment of emergency cases in hospitals as well as serving long-time or more profitable customers first.

(3) Shortest processing time rule


This model minimizes the average waiting time by putting the shortest job first and the longest job last. This does not impact the total working time but ensures, that those customers with the shortest working times get served more quickly. The problem with this model is, that it gives customers an incentive to claim false processing times (“I need only five minutes.”) in order to get served fist. For this reason, this rule is usually only used in manufacturing or in digital processing, where processing times can be accurately predicted (and no human customers are directly involved).

(4) Appointment rules


Some business are trying to solve the problem of customers not showing up in regular intervals by forcing them to show up in regular intervals through appointments. However, this model is quite impractical for many business (think about fast food restaurants or supermarkets) and downright impossible for others (think about emergency room treatment where individual demand is nearly impossible to predict).



These lecture notes were taken during 2013 installment of the MOOC “An Introduction to Operations Management” taught by Prof. Dr. Christian Terwiesch of the Wharton Business School of the University of Pennsylvania at Coursera.org.

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